States_Choropleth
rm(list=ls())
# Normal Choroplath libs
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.4 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(dplyr)
library(urbnmapr)
library(tmap)
library(sf)## Warning: package 'sf' was built under R version 4.0.5## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1library(stringr)
# Leaflet Choroplath libs
library(leaflet)
library(tigris)## Warning: package 'tigris' was built under R version 4.0.5## To enable
## caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.#webscrapping
library(rvest)##
## Attaching package: 'rvest'## The following object is masked from 'package:readr':
##
## guess_encodingdata <- read.csv("state_2020.csv")
data_1 <- data %>%
select(State, Debt.Ratio..Total.Debts.Total.Assets) %>%
rename("Debt_Level" = "Debt.Ratio..Total.Debts.Total.Assets")
#Debt was recorded as a character, converted to numeric
data_1$Debt_Level <- str_remove_all(string = data_1$Debt_Level, pattern = "%")
data_1$Debt_Level <- as.numeric(data_1$Debt_Level)
# removing DC as it is a district
states_sf <- get_urbn_map("states", sf = TRUE)
states_sf <- states_sf %>%
filter(state_abbv != "DC")# Debt levels by state
states_sf <- states_sf %>%
rename(State = "state_name")
state_debt <- left_join(data_1, states_sf, by = "State")
head(state_debt)## State Debt_Level state_fips state_abbv geometry
## 1 Illinois 468.7 17 IL MULTIPOLYGON (((720075.7 -4...
## 2 New Jersey 441.7 34 NJ MULTIPOLYGON (((2057792 -28...
## 3 Connecticut 334.9 09 CT MULTIPOLYGON (((2156197 -83...
## 4 Massachusetts 305.5 25 MA MULTIPOLYGON (((2416684 359...
## 5 New York 273.8 36 NY MULTIPOLYGON (((2282929 -20...
## 6 Delaware 174.2 10 DE MULTIPOLYGON (((2056914 -28...state_debt <- st_as_sf(state_debt)
position <- c("center", "top")
tm_shape(state_debt) +
tm_polygons(col = "Debt_Level", title = "Debt Level", palette = "Purples", breaks = c(0,25,50,75, 100, 150,200,300,400, 500)) +
tm_text(text = "state_abbv", size = .75, remove.overlap = TRUE) +
tm_legend(scale = .80) +
tm_layout(title = "State Debt Levels 2020 (%)", title.position = position) 
# webscraping credit ratings of each state from wikipedia
credit <- read_html("https://en.wikipedia.org/wiki/List_of_U.S._states_by_credit_rating")
credit_scores <- credit %>%
html_nodes("table") %>%
.[[3]] %>%
html_table(fill = TRUE)
credit_scores <- credit_scores %>%
select(State, '2020') %>%
filter(State != "Source:[2]") %>%
rename(Credit_Rating = "2020")
data_1 <- left_join(data_1, credit_scores, by = "State")# Ask
leaflet_map <- states(cb = TRUE)##
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|======================================================================| 100%# Getting rid of US Territories
ter <- list("VI", "DC", "MP", "GU", "AS", "PR")
leaflet_map <- leaflet_map %>%
filter(!STUSPS %in% ter) %>%
rename(State = "NAME")
leaflet_debt <- left_join(data_1, leaflet_map, by = "State")
leaflet_debt <- st_as_sf(leaflet_debt)
content <- paste("State:", leaflet_debt$State, "<br/>",
"Debt Level:", leaflet_debt$Debt_Level, "<br/>",
"Credit Rating:", leaflet_debt$Credit_Rating, "<br/>")
colors <- colorNumeric("Purples", domain = leaflet_debt$Debt_Level)
# Any ideas how to make the scale stronger and brighter
leaflet(leaflet_debt) %>%
addProviderTiles("Stamen.Toner") %>%
setView( lng = -95.71289, lat = 37.09024, zoom = 3) %>%
addPolygons(fillColor = ~colors(leaflet_debt$Debt_Level),
fillOpacity = 4,
popup = ~content,
label = leaflet_debt$State) %>%
addLegend(pal = colors,
values = leaflet_debt$Debt_Level,
position = "bottomright",
title = "Debt Level (%)") ## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'